1. Field of the Invention
The present invention relates to methods of gathering and analyzing emergency service data for the purposes of forecasting future emergency medical service requirements, allocating resources, and generating work schedules.
2. Description of the Prior Art
The logistics of managing emergency medical services presents a challenging set of problems. Schedulers are faced with the task of providing sufficient resources to meet randomly-occurring requests for service, where delays in response are unacceptable, yet must increasingly be concerned with the overall cost of such services. Emergency medical services are labor intensive, with typically 80% or more of the operating costs being manpower. Since service performance levels are often mandated by law, with requirements that a certain percentage of calls be handled within a given response time, schedulers must also find ways to anticipate the effects of their resource allocation and scheduling decisions on actual performance levels in the field. The past 25 years have seen the increased application of Operations Research and Management Science to these public safety and emergency medical services problems (Kolesar, P., & Swersey, A. J. [1986] "The deployment of urban emergency units: a survey," TIMS Studies in the Management Studies 22, pp. 87-119).
The demand for emergency medical services has the characteristics of the essentially random occurrence of individual calls, but with demand levels which recur in historically discernible patterns. The time required to service each call is long with respect to the scheduling increment of crew shifts, typically ranging from 15 minutes for a call not requiring transport to well over an hour (prior art scheduling methods often assume each call lasts one hour). Emergency medical services providers have generally divided the week into 168 one hour increments, and tracked incoming calls on that basis. There can be significant changes in the historical level of demand from one hour to the next, such as a peak in demand in the hour after bars close, followed by a rapid decline. Since calls represent essentially random events, data on the level of demand must be collected over a period of weeks or months to accurately quantify the level of demand.
In recent years many local governments have contracted out all or part of their emergency medical services to private providers. The private providers are required to meet specific standards with respect to call response times, typically expressed as a percentage of calls in which a unit must arrive "on scene" within a specified response time (e.g., 95% of calls responded to within 10 minutes). To be competitive in the marketplace, both for the purposes of competitive bidding on future contracts and for maintaining profitability of existing contracts, providers must have information on historical levels of demand that allow them to accurately forecast future demand and provide adequate levels of staffing, without incurring unnecessary costs. A more accurate method of measuring demand, together with more sophisticated scheduling methods, can be a major marketplace advantage.
Two constraints on the gathering of utilization data have in the past been the level of expertise of the personnel involved in the dispatch operation and the limitations of past Computer Aided Dispatch ("CAD") systems. In smaller service areas, the dispatcher may be a driver or Emergency Medical Technician (EMT) who serves part time as dispatcher; in larger service areas, full time dispatchers may be employed. The dispatchers will generally be trained as an EMT, but are unlikely to have an understanding of statistics or higher mathematics. Making a record of the number of calls received per hour is a task which is easily understood and which can be performed by personnel of any experience level, without the need for advanced training; hence the EMS industry's adoption of calls received as the basis for generating usage, allocation and scheduling reports. Computer Aided Dispatch (CAD) systems have the capability to collect and store much more detailed information on each call received, however, this information has generally not been well-utilized by the industry.